predict.glmgraph {glmgraph} | R Documentation |
Similar to other predict methods, this function returns predictions from
a fitted "glmgraph"
object.
## S3 method for class 'glmgraph' predict(object, X, type=c("response", "coefficients", "class", "nzeros","link"), lambda1, lambda2,...)
object |
Fitted |
X |
Matrix of values at which predictions are to be made. |
lambda1 |
Values of the regularization parameter |
lambda2 |
Values of the regularization parameter |
type |
Type of prediction: |
... |
Other parameters to |
Li Chen <li.chen@emory.edu> , Jun Chen <chen.jun2@mayo.edu>
Li Chen. Han Liu. Hongzhe Li. Jun Chen. (2015) glmgraph: Graph-constrained Regularization for Sparse Generalized Linear Models.(Working paper)
glmgraph
set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 ## construct laplacian matrix from adjacency matrix A <- matrix(rep(0,p*p),p,p) A[1:p1,1:p1] <- 1 A[(p1+1):p,(p1+1):p] <- 1 diag(A) <- 0 btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL ### gaussian Y <- eta+rnorm(n) obj <- glmgraph(X,Y,L) res <- predict(obj, X, type="link", lambda1=0.05,lambda2=0.01) res <- predict(obj, X, type="response", lambda1=0.05,lambda2=0.01) res <- predict(obj,X,type="nzeros",lambda1=0.05,lambda2=0.01) ### binomial Y <- rbinom(n,1,prob=1/(1+exp(-eta))) obj <- glmgraph(X,Y,L,family="binomial") res <- predict(obj,X,type="class",lambda1=c(0.05,0.06),lambda2=c(0.02,0.16,0.32))